Measurement study of low bitrate internet video streaming
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Measurement Study of Low-bitrate Internet Video Streaming. Dmitri Loguinov Hayder Radha. City University of New York and Philips Research. Motivation. Market research by Telecommunications Reports International (TRI) from August 2001

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Measurement study of low bitrate internet video streaming

Measurement Study of Low-bitrate Internet Video Streaming

Dmitri LoguinovHayder Radha

City University of New York and Philips Research



  • Market research by Telecommunications Reports International (TRI) from August 2001

  • The report estimates that out of 70.6 million households in the US with Internet access, an overwhelming majority use dialup modems (Q2 of 2001)

Motivation cont d

Motivation (cont’d)

  • Consider broadband (cable, DSL, and satellite) Internet access vs. narrowband (dialup and webTV)

  • 89% of households use modems and 11% broadband

Motivation cont d1

Motivation (cont’d)

  • Customer growth in Q2 of 2001:

    • +5.2% dialup

    • +0.5% cable modem

    • +29.7% DSL

    • Even though DSL grew fast in early 2001, the situation is now different with many local companies going bankrupt and TELCOs increasing the prices

  • A report from Cahners In-Stat Group found that despite strong forecasted growth in broadband access, most households will still be using dial-up access in the year 2005 (similar forecast in Forbes ASAP, Fall 2001, by IDC market research)

  • Furthermore, 30% of US households state that they still have no need or desire for Internet access

Motivation cont d2

Motivation (cont’d)

  • Our study was conducted in late 1999 – early 2000

  • At that time, even more Internet users connected through dialup modems, which sparked our interest in using modems as the primary access technology for out study

  • However, even today, our study could be considered up-to-date given the numbers from recent market research reports

  • In fact, the situation is unlikely to change until year 2005

  • Our study examines the performance of the Internet from the angle of an average Internet user

Motivation cont d3

Motivation (cont’d)

  • Note that previous end-to-end studies positioned end-points at universities or campus networks with typically high-speed (T1 or faster) Internet access

  • Hence, the performance of the Internet perceived by a regular home user remains largely undocumented

  • Rather than using TCP or ICMP probes, we employed real-time video traffic in our work, because performance studies of real-time streaming in the Internet are quite scarce

  • The choice of NACK-based flow control was suggested by RealPlayer and Windows MediaPlayer, which dominate the current Internet streaming market

Overview of the talk

Overview of the Talk

  • Experimental Setup

  • Overview of the Experiment

  • Packet Loss

  • Underflow Events

  • Round-trip Delay

  • Delay Jitter

  • Packet Reordering

  • Asymmetric Paths

  • Conclusion

Experimental setup

Experimental Setup

  • MPEG-4 client-server real-time streaming architecture

  • NACK-based retransmission and fixed streaming bitrate (i.e., no congestion control)

  • Stream S1 at 14 kb/s (16.0 kb/s IP rate), Nov-Dec 1999

  • Stream S2 at 25 kb/s (27.4 kb/s IP rate), Jan-May 2000



  • Three ISPs (Earthlink, AT&T WorldNet, IBM Global Net)

  • Phone database included 1,813 dialup points in 1,188 cities

  • The experiment covered 1,003 points in 653 US cities

  • Over 34,000 long-distance phone calls

  • 85 million video packets, 27.1 GBytes of data

  • End-to-end paths with 5,266 Internet router interfaces

  • 51% of routers from dialup ISPs and 45% from UUnet

  • Sets D1p and D2p contain successful sessions with streams S1 and S2, respectively

Overview cont d

Overview (cont’d)

  • Cities per state that participated in the experiment

Overview cont d1

Overview (cont’d)

  • Streaming success rate during the day shown below

  • Varied between 80% during the night (midnight – 6 am) to 40% during the day (9 am – noon)

Overview cont d2

Overview (cont’d)

  • Average end-to-end hop count 11.3 in D1p and 11.9 in D2p

  • The majority of paths contained between 11 and 13 hops

  • Shortest path – 6 hops, longest path – 22 hops

Packet loss

Packet Loss

  • Average packet loss was 0.5% in both datasets

  • 38% of 10-min sessions with no packet loss

  • 75% with loss below 0.3%

  • 91% with loss below 2%

  • During the day, average packet loss varied between 0.2% (3 am - 6 am) and 0.8% (9am - 6pm EDT)

  • Per-state packet loss varied between 0.2% (Idaho) to 1.4% (Oklahoma), but did not depend on the average RTT or the average number of end-to-end hops in the state (correlation –0.16 and –0.04, respectively)

Packet loss cont d

Packet Loss (cont’d)

Packet loss cont d1

Packet Loss (cont’d)

  • 207,384 loss bursts and 431,501 lost packets

  • Loss burst lengths (PDF and CDF):

Packet loss cont d2

Packet Loss (cont’d)

  • Most bursts contained no more than 5 packets (however, the tail reached to over 100 packets)

  • RED was disabled in the backbone; still 74% of loss bursts contained only 1 packet apparently dropped in FIFO queues

  • Average burst length was 2.04 packets in D1p and 2.10 packets in D2p

  • Conditional probability of packet loss was 51% and 53%, respectively

  • Over 90% of loss burst durations were under 1 second (maximum 36 seconds)

  • The average distance between lost packets was 21 and 27 seconds, respectively

Packet loss cont d3

Packet Loss (cont’d)

  • Apparently heavy-tailed distributions of loss burst lengths

  • Pareto with  = 1.34; however, note that data was non-stationary (time of day or access point non-stationarity)

Underflow events

Underflow events

  • Missing packets (frames) at their decoding deadlines cause buffer underflows at the receiver

  • Startup delay used in the experiment was 2.7 seconds

  • 63% (271,788) of all lost packets were discovered to be missing before their deadlines

  • Out of these 63% of lost packets:

    • 94% were recovered in time

    • 3.3% were recovered late

    • 2.1% were never recovered

  • Retransmission appears quite effective in dealing with packet loss, even in the presence of large end-to-end delays

Underflow events cont d

Underflow events (cont’d)

  • 37% (159,713) of lost packets were discovered to be missing after their deadlines had passed

  • This effect was caused by large one-way delay jitter

  • Additionally, one-way delay jitter caused 1,167,979 data packets to be late for decoding

  • Overall, 1,342,415 packets were late (1.7% of all sent packets), out of which 98.9% were late due to large one-way delay jitter rather than due to packet loss combined with large RTT

  • All late packets caused the “freeze-frame” effect for 10.5 seconds on average in D1p and 8.5 seconds in D2p (recall that each session was 10 minutes long)

Underflow events cont d1

Underflow events (cont’d)

  • 90% of late retransmissions missed the deadline by no more than 5 seconds, 99% by no more than 10 seconds

  • 90% of late data packets missed the deadline by no more than 13 seconds, 99% by no more than 27 seconds

Round trip delay

Round-trip Delay

  • 660,439 RTT samples

  • 75% of samples below 600 ms and 90% below 1 second

  • Average RTT was 698 ms in D1p and 839 ms in D2p

  • Maximum RTT was over 120 seconds

  • Data-link retransmission combined with low-bitrate connection were responsible for pathologically high RTTs

  • However, we found access points with 6-7 second IP-level buffering delays

  • Generally, RTTs over 10 seconds were considered pathological in our study

Round trip delay cont d

Round-trip Delay (cont’d)

  • Distributions of the RTT in both datasets (PDF) were similar and contained a very long tail

Round trip delay cont d1

Round-trip Delay (cont’d)

  • Distribution tails closely matched hyperbolic distributions (Pareto with  between 1.16 and 1.58)

Round trip delay cont d2

Round-trip Delay (cont’d)

  • The average RTT varied during the day between 574 ms (3 am – 6 am) and 847 ms (3 pm – 6 pm) in D1p

  • Between 723 ms and 951 ms in D2p

  • Relatively small increase in the RTT during the day (by only 30-45%) compared to that in packet loss (by up to 300%)

  • Per-state RTT varied between 539 ms (Maine) and 1,053 ms (Alaska); Hawaii and New Mexico also had average RTTs above 1 second

  • Little correlation between the RTT and geographical distance of the state from NY

  • However, much stronger positive correlation between the number of hops and the average state RTT:  = 0.52

Delay jitter

Delay Jitter

  • Delay jitter is one-way delay variation

  • Positive values of delay jitter are packet expansion events

  • 97.5% of positive samples below 140 ms

  • 99.9% under 1 second

  • Highest sample 45 seconds

  • Even though large delay jitter was not frequent, once a single packet was delayed by the network, the following packets were also delayed creating a “snowball” of late packets

  • Large delay jitter was very harmful during the experiment

Packet reordering

Packet Reordering

  • Average reordering rates were low, but noticeable

  • 6.5% of missing packets (or 0.04% of sent) were reordered

  • Out of 16,852 sessions, 1,599 (9.5%) experienced at least one reordering event

  • The highest reordering rate per ISP occurred in AT&T WorldNet, where 35% of missing packets (0.2% of sent packets) were reordered

  • In the same set, almost half of the sessions (47%) experienced at least one reordering event

  • Earthlink had a session where 7.5% of sent packets were reordered

Packet reordering cont d

Packet Reordering (cont’d)

  • Reordering delay Dr is time between detecting a missing packet and receiving the reordered packet

  • 90% of samples Dr below 150 ms, 97% below 300 ms, 99% below 500 ms, and the maximum sample was 20 seconds

Packet reordering cont d1

Packet Reordering (cont’d)

  • Reordering distance is the number of packets received during the reordering delay (84.6% of the time a single packet, 6.5% exactly 2 packets, 4.5% exactly 3 packets)

  • TCP’s triple-ACK avoids 91.1% of redundant retransmits and quadruple-ACK avoids 95.7%

Path asymmetry

Path Asymmetry

  • Asymmetry detected by analyzing the TTL of the returned packets during the initial traceroute

  • Each router reset the TTL to a default value (such as 255) when sending a “TTL expired” ICMP message

  • If the number of forward and reverse hops was different, the path was “definitely asymmetric”

  • Otherwise, the path was “possibly (or probably) symmetric”

  • No fail-proof way of establishing path symmetry using end-to-end measurements (even using two traceroutes in reverse directions)

Path asymmetry cont d

Path Asymmetry (cont’d)

  • 72% of sessions operated over definitely asymmetric paths

  • Almost all paths with 14 or more end-to-end hops were asymmetric

  • Even the shortest paths (with as low as 6 hops) were prone to asymmetry

  • “Hot potato” routing is more likely to cause asymmetry in longer paths, because they are more likely to cross AS borders than shorter paths

  • Longer paths also exhibited a higher reordering probability than shorter paths



  • Dialing success rates were quite low during the day (as low as 40%)

  • Retransmission worked very well even for delay-sensitive traffic and high-latency end-to-end paths

  • Both RTT and packet-loss bursts appeared to be heavy-tailed

  • Our clients experienced huge end-to-end delays both due to large IP buffers as well as persistent data-link retransmission

  • Reordering was frequent even given our low bitrates

  • Most paths were in fact asymmetric, where longer paths were more likely to be identified as asymmetric

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